MULTI-LAYER COUPLING RELATIONSHIP-BASED TRAIN OPERATION DEVIATION PROPAGATION CONDITION RECOGNITION METHOD

Abstract

The present invention relates to a multi-layer coupling relationship-based train operation deviation propagation condition recognition method, where the method includes the following steps: (1) recognizing an effective train event time sequence, including an arrival event and a departure event of a train at each passing station; (2) uniformly extracting train activity data, including a stop activity, a section operation activity, a turn-back activity, and an arrival or departure interval activity; (3) constructing coupling relationship groups between a train event and a train activity and between train activities; and (4) performing statistics on changes of train operation deviation in each relationship group, and outputting a respective distribution function and a time-space distribution visualized result. Compared with the prior art, the present invention has the advantages of being practical, automatic recognition, feedback optimization, and the like.

Claims

1. A multi-layer coupling relationship-based train operation deviation propagation condition recognition method, comprising the following steps: (1) recognizing an effective train event time sequence, comprising an arrival event and a departure event of a train at each passing station; (2) uniformly extracting train activity data, comprising a stop activity, a section operation activity, a turn-back activity, and an arrival or departure interval activity; (3) constructing coupling relationship groups between a train event and a train activity and between train activities; and (4) performing statistics on changes of train operation deviation in each relationship group, and outputting a respective distribution function and a time-space distribution visualized result.

2. The multi-layer coupling relationship-based train operation deviation propagation condition recognition method according to claim 1, wherein the effective train event time sequence is specifically an effective event time sequence obtained by removing an abnormal value caused by a system error according to train operation data provided by an urban rail transit automatic train supervision system ATS, deleting data for an abnormal stop, thus obtaining effective event data, and sorting the effective event data according to type requirements of train activities to be extracted.

3. The multi-layer coupling relationship-based train operation deviation propagation condition recognition method according to claim 2, wherein the type requirements of the train activities are specifically as follows: to extract the train stop activity, the section operation activity, and the turn-back activity, the effective event data needs to be sorted in ascending order according to a date, a train number, and a time of occurrence, thus obtaining a time sequence 1 of an arrival event and a departure event of a train at each station; and to extract the arrival or departure interval activity, the effective event data needs to be sorted in ascending order according to a date, a station, a direction, and a time of occurrence, thus obtaining a time sequence 2 of an arrival event and a departure event of a train at each station.

4. The multi-layer coupling relationship-based train operation deviation propagation condition recognition method according to claim 3, wherein each train activity is formed by two associated train events and is specifically as follows: according to the time sequence 1 of the arrival event and the departure event of the train at each station, adjacent arrival-departure events in the same direction form the stop activity, adjacent departure-arrival events or departure-departure events in the same direction form the section operation activity, and adjacent departure-arrival events or arrival-departure events in an opposite direction form the turn-back activity; and according to the time sequence 2 of the arrival event and the departure event of the train at each station, adjacent arrival-arrival events in the same direction form the arrival interval activity, and adjacent departure-departure events in the same direction form the departure interval activity.

5. The multi-layer coupling relationship-based train operation deviation propagation condition recognition method according to claim 4, wherein the coupling relationship group between the train event and the train activity specifically comprises: a relationship group between the arrival event and an activity associated with the arrival event, including a relationship between an arrival event of a train at a station and a stop activity of the train, and a relationship between the arrival event and an arrival interval activity of a subsequent train; and a relationship group between the departure event and an activity associated with the departure event, including a relationship between a departure event of a train at a station and a subsequent section operation activity of the train and a relationship between the departure event and a departure interval activity of a previous train at a subsequent station.

6. The multi-layer coupling relationship-based train operation deviation propagation condition recognition method according to claim 4, wherein the coupling relationship group between the train activities specifically comprises: a relationship group between adjacent activities of the same train, comprising: a relationship between a stop activity of the same train at a station and an operation activity of the train between two sections before and after the train, a relationship between an operation activity of the train in one section and a stop activity of the train at two stations before and after the train, and a relationship among an end-to-stop activity when turning back after arriving a station, a rail transferring activity, and a departure stop activity; and a relationship group between adjacent activities of adjacent trains, including: a relationship between a stop activity of a train at a station and a departure interval activity between two trains before and after the train and the train, and a relationship between an operation activity of a train in a section and an arrival interval activity between two trains before and after the train and the train at a subsequent station.

7. The multi-layer coupling relationship-based train operation deviation propagation condition recognition method according to claim 1, wherein the changes of train operation deviation in each relationship group specifically comprise: for the relationship between the activity and the event, statistically fitting a distribution function of activity time deviation changing with the event time deviation; and for the relationship between the activities, counting a degree of change for time deviation of each group of associated activities in each time period and each line section.

8. The multi-layer coupling relationship-based train operation deviation propagation condition recognition method according to claim 7, wherein the time periods comprise: an early flat peak, an early high peak, a noon flat peak, a late high peak, a late flat peak, and a night flat peak.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0034] FIG. 1 is a schematic diagram of an activity-event coupling relationship of the present invention; and

[0035] FIG. 2 is a flowchart of the present invention;

DESCRIPTION OF THE EMBODIMENTS

[0036] Clear and complete description will be made to the technical solutions in embodiments of the present invention in conjunction with drawings in the embodiments of the present invention hereafter. Obviously, the described embodiments are merely a part of embodiments of the present invention and not all the embodiments. Based on the embodiments of the present invention, all of other embodiments obtained by a person of ordinary skill in the art without any creative effort shall belong to the protection scope of the present invention.

[0037] According to the method in the present invention, an effective train event time sequence is uniformly recognized and screened according to a current urban rail transit train operation collection state. Various train activity data is extracted respectively based on a train event time sequence sorted according to a train number or according to a station respectively. Considering a coupling relationship group between a plurality of events and a plurality of activities, statistics is performed on changes of train operation deviation in each relationship group, and a respective distribution function and a time-space distribution visualized result are outputted, thus obtaining a propagation condition of the train operation deviation in the space-time range.

[0038] The present invention is further described below, and the method of the present invention includes the following steps (FIG. 2):

[0039] 1. Recognize effective train event data. Step 1 mainly includes screening data of an arrival data and a departure event of a train at a normal stop, and sorting the data according to a specified condition, thus obtaining an event time sequence. An existing commonly used data format is shown in Table 1:

TABLE-US-00001 TABLE 1 TRAIN_ DESTINATION_ GROUP_ LOCAL_ GLOBAL_ TRAIN_ STATION ID CODE TRAIN_ SUB_ID SUB_ID ATTRIBUTE ID PLAT- ARRIVAL_ DATE_ TIME_ DATE _ TIME_ TIME_ FORM DEPARTURE_ VALUE VALUE VALUE_ VALUE_ DIFF_ FLAG EXPECTED EXPECTED FROM_ SCHD

[0040] 2. Extract train activity data. Various train activities are calculated and distinguished according to the train event sequence, and the train activities mainly include a train section operation activity, a train stop activity, a train turn-back activity, and a train operation interval activity. Each activity is formed by two associated events, which are an arrival event and a departure event. Herein, in the present invention, data field in Table 1 represents arrival event information and departure event information of a train activity and a data field of a formed activity that is defined in Table 2. Table 1 and Table 2 form the train activity data format together.

TABLE-US-00002 TABLE 2 TO_STATION TO_PLATFORM TO_TIME_VALUE TO_VALUE_ TO_DIFF_ EXPECTED FROM_SCHD DURA_TYPE DURA_DIRECTION DURA_VALUE DURA_VALUE_ DURA_DIFF_ EXPECTED FROM_SCHD

[0041] 3. Construct coupling relationship groups between a train activity and a train event and between train activities. A relationship group between a train activity and a train event includes a relationship group between an arrival event and associated activities before and after the arrival event, and a relationship group between a departure event and associated activities before and after the departure event. A relationship group between train activities includes a relationship group between a stop activity and associated activities before and after the stop activity, a relationship group between a section operation activity and associated activities before and after the section operation activity, and a relationship between a rail transferring activity and two stop activities before and after the rail transferring activity. The associated activities include adjacent activities of the same train and adjacent activities of adjacent trains.

[0042] 4. Perform statistics on changes of train operation deviation in each relationship group. It mainly includes a distribution function of activity time deviation changing with event time deviation, and a combined change of time deviation of each group of associated activities in different time-space ranges.

[0043] The event data includes event time deviation data (Table 1), and the extracted activity data includes activity time deviation data (Table 2). Associated deviation data is retrieved based on the coupling relationships in step 3 and statistical analysis is performed, so that the distribution function of the activity time deviation changing with the event time deviation within a custom range and a time-space distribution virtualized result of associated activity time deviation can be displayed.

[0044] The above descriptions are only specific implementations of the present invention. However, the protection scope of the present invention is not limited thereto, any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present invention, and all of these modifications or substitutions shall fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined with reference to the appended claims.